Publications
2016
1.
González-Castro, Víctor; del Carmen Valdés-Hernández, María; Armitage, Paul A; Wardlaw, Joanna M
Automatic rating of perivascular spaces in brain MRI using bag of visual words Artículo de revista
En: Image Analysis and Recognition: 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016, Proceedings 13, pp. 642–649, 2016, (Publisher: Springer International Publishing).
Resumen | Enlaces | BibTeX | Etiquetas: machine learning, MRI, neurological disorders, perivascular spaces
@article{gonzalez-castro_automatic_2016,
title = {Automatic rating of perivascular spaces in brain MRI using bag of visual words},
author = {Víctor González-Castro and María del Carmen Valdés-Hernández and Paul A Armitage and Joanna M Wardlaw},
url = {https://link.springer.com/chapter/10.1007/978-3-319-41501-7_72},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {Image Analysis and Recognition: 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016, Proceedings 13},
pages = {642–649},
abstract = {This paper presents a fully automatic method for assessing perivascular space (PVS) burden in the basal ganglia using structural MRI. A Support Vector Machine classifier, combined with a Bag of Visual Words (BoW) model, describes the region using two local descriptor approaches: SIFT and textons. The method achieves an accuracy of 82.34% with SIFT and 79.61% with textons, aiding in the study of neurological conditions linked to enlarged PVS.},
note = {Publisher: Springer International Publishing},
keywords = {machine learning, MRI, neurological disorders, perivascular spaces},
pubstate = {published},
tppubtype = {article}
}
This paper presents a fully automatic method for assessing perivascular space (PVS) burden in the basal ganglia using structural MRI. A Support Vector Machine classifier, combined with a Bag of Visual Words (BoW) model, describes the region using two local descriptor approaches: SIFT and textons. The method achieves an accuracy of 82.34% with SIFT and 79.61% with textons, aiding in the study of neurological conditions linked to enlarged PVS.